SARCASM DETECTION
BEYOND USING LEXICAL FEATURES
ADEWUYI, Joseph
Oluwaseyi1 and OLADEJI, Ifeoluwa David2
Department of
Computer Science, University of Ibadan, Ibadan, Nigeria.
ABSTRACT
In current time, social media
plateforms such as facebook, twitter, and so forth have improved and received
substantial importance. These websites have grown into huge environments
wherein users explicit their thoughts, perspectives and reviews evidently.
Organizations leverage this environment to tap into people’s opinion on their
services and to make a quick feedback. This research seeks to keep away from
using grammatical words as the only features for sarcasm detection however also
the contextual features, which are theories explaining when, how and why sarcasm
is expressed. A deep neural network architecture model was employed to carry
out this task, which is a bidirectional long short-term memory with conditional
random fields (Bi-LSTM-CRF), two stages were employed to classify if a reply or
comment to a tweet is sarcastic or not-sarcastic. The performance of the models
was evaluated using the following metrics: Accuracy, Precision, Recall,
F-measure.
KEYWORDS
Sarcasm Detection, Deep Learning,
Contextual features
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